This paper introduces a method that improves the performance of a problem solver by reformulating its domain theory into one in which functionally relevant features are explicit in the syntax. This method, in contrast to previous reformulation methods, employs sets of training examples to constrain and direct the reformulation process....
The task of inductive learning from examples places constraints on the representation of training instances and concepts. These constraints are different from, and often incompatible with, the constraints placed on the representation by the performance task. This incompatibility is severe when learning functional concepts and explains why previous researchers have...
Knowledge compilation improves search-intensive problem-solvers that are easily specified but inefficient. One promising approach improves efficiency by constructing a database of problem-instance/best-action pairs that replace problem-solving search with efficient lookup. The database is constructed by reverse enumeration- expanding the complete search space backwards, from the terminal problem instances. This approach...
This technical report reprints two articles that appeared in Proceedings of the Third International Machine Learning Workshop at Skytop, Pennsylvania, June 24-26, 1985. The first paper, The EG Project: Recent Progress, summarizes work on the EG project, which is investigating the role of active experimentation in aiding machine learning programs....
This paper formalizes a new learning from examples problem: identifying a correct concept definition from positive examples such that the concept is some specialization of a target concept defined by a domain theory. This paper describes an empirical study that evaluates three methods for solving this problem: explanation based generalization...
We introduce five criteria by which to judge the suitability of a method for solving the problem of learning concepts from examples: correctness (the correct concept should be identified), performance efficiency (the learned definition should be efficient to apply to the performance task), flexibility (the method should be able to...
The task of inductive learning from examples places constraints on the representation of training instances and concepts. These constraints are different from, and often incompatible with, the constraints placed on the representation by the performance task. This incompatibility explains why previous researchers have found it so difficult to construct good...
This report reviews the 31 papers on machine learning that were presented at the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85) held in Los Angeles during August, 1985. The papers are grouped according to a taxonomy of the various subareas of machine learning research. The areas receiving the most...
Two powerful reasoning tools have recently appeared, logic programming and assumption based truth maintenance systems (ATMS). An ATMS offers significant advantages to a problem solver: assumptions are easily managed and the search for solutions can be carried out in the most general context first and in any order. Logic programming...